Acute Crit Care.  2024 Nov;39(4):611-620. 10.4266/acc.2024.00752.

Early detection of bloodstream infection in critically ill children using artificial intelligence

• Han HJ
• Kim K
• Park JD

• KMID: 2563550
• DOI: http://doi.org/10.4266/acc.2024.00752

Abstract

Background
Despite the high mortality associated with bloodstream infection (BSI), early detection of this condition is challenging in critical settings. The objective of this study was to create a machine learning tool for rapid recognition of BSI in critically ill children.
Methods
Data were extracted from a derivative cohort comprising patients who underwent at least one blood culture during hospitalization in the pediatric intensive care unit (PICU) of a tertiary hospital from January 2020 to June 2023 for model development. Data from another tertiary hospital were utilized for external validation. Variables selected for model development were age, white blood cell count with segmented neutrophil count, C-reactive protein, bilirubin, liver enzymes, glucose, body temperature, heart rate, and respiratory rate. Algorithms compared were extra trees, random forest, light gradient boosting, extreme gradient boosting, and CatBoost.
Results
We gathered 1,806 measurements and recorded 290 hospitalizations from 263 patients in the derivative cohort. Median age on admission was 43 months, with an interquartile range of 10–118.75 months, and a male predominance was observed (n=160, 55.2%). Candida albicans was the most prevalent pathogen, and median duration to confirm BSI was 3 days (range, 3–4). Patients with BSI experienced significantly higher in-hospital mortality and prolonged stays in the PICU than patients without BSI. Random forest classifier achieved the highest area under the receiver operating characteristic curve of 0.874 (0.762 for the validation set).
Conclusions
We developed a machine learning model that predicts BSI with acceptable performance. Further research is necessary to validate its effectiveness.

Keyword

bloodstream infection; machine learning; sepsis

Figure

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  Figure 1. Flowchart of patient selection for model development and validation.

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  Figure 2. Correlation matrix of the derivative cohort. To reduce multicollinearity, variables with lower correlation with bloodstream infection were eliminated if the correlation coefficient between any two variables exceeded 0.5. PLT: platelet; WBC: white blood cell count; BT: body temperature; HR: heart rate; RR: respiratory rate; BIL: bilirubin; AST: aspartate transaminase; ALT: alanine transaminase; CRP: C-reactive protein; SEG: segmented neutrophil; BSI: bloodstream infection.

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  Figure 3. Predictive performance of the random forest model. (A) A confusion matrix of the derivative and validation cohorts. (B) Receiver operating characteristic curve. (C) Precision-recall curve were generated to evaluate the best-performing model. AUROC: area under the receiver operating curve.

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  Figure 4. Feature importance of the random forest model. Feature importance was analyzed to determine the contributions of each feature to the performance of the model. (A) Mean decrease in impurity and (B) mean permutation importance were calculated and are plotted as bar graphs. Age (Age_m), C-reactive protein (CRP), alanine transaminase (ALT), bilirubin (BIL), and respiratory rate (RR) were given higher importance. MDI: mean decrease in impurity; WBC: white blood cell count; BT: body temperature; HR: heart rate; SEG: segmented neutrophil.

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  Figure 5. Receiver operating characteristic curves of the machine learning models. Receiver operating characteristic curves for (A) The extreme gradient boosting model, (B) the extra trees classifier model, (C) the CatBoost model, and (D) the light gradient boosting model. AUROC: area under the receiver operating curve.

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